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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include <armnnUtils/Permute.hpp>
#include <armnnUtils/QuantizeHelper.hpp>
#include <ResolveType.hpp>
#include <CommonTestUtils.hpp>
#include <map>
#include <vector>
namespace
{
armnn::INetworkPtr CreateResizeNetwork(const armnn::ResizeDescriptor& descriptor,
const armnn::TensorInfo& inputInfo,
const armnn::TensorInfo& outputInfo)
{
using namespace armnn;
INetworkPtr network(INetwork::Create());
IConnectableLayer* input = network->AddInputLayer(0, "input");
IConnectableLayer* resize = network->AddResizeLayer(descriptor, "resize");
IConnectableLayer* output = network->AddOutputLayer(0, "output");
Connect(input, resize, inputInfo, 0, 0);
Connect(resize, output, outputInfo, 0, 0);
return network;
}
template<armnn::DataType ArmnnType>
void ResizeEndToEnd(const std::vector<armnn::BackendId>& backends,
armnn::DataLayout dataLayout,
armnn::ResizeMethod resizeMethod)
{
using namespace armnn;
using T = ResolveType<ArmnnType>;
constexpr unsigned int inputWidth = 3u;
constexpr unsigned int inputHeight = inputWidth;
constexpr unsigned int outputWidth = 5u;
constexpr unsigned int outputHeight = outputWidth;
TensorShape inputShape = MakeTensorShape(1, 1, inputHeight, inputWidth, dataLayout);
TensorShape outputShape = MakeTensorShape(1, 1, outputHeight, outputWidth, dataLayout);
const float qScale = IsQuantizedType<T>() ? 0.25f : 1.0f;
const int32_t qOffset = IsQuantizedType<T>() ? 50 : 0;
TensorInfo inputInfo(inputShape, ArmnnType, qScale, qOffset, true);
TensorInfo outputInfo(outputShape, ArmnnType, qScale, qOffset);
std::vector<float> inputData =
{
1.f, 2.f, 3.f,
4.f, 5.f, 6.f,
7.f, 8.f, 9.f
};
std::vector<float> expectedOutputData;
switch(resizeMethod)
{
case ResizeMethod::Bilinear:
{
expectedOutputData =
{
1.0f, 1.6f, 2.2f, 2.8f, 3.0f,
2.8f, 3.4f, 4.0f, 4.6f, 4.8f,
4.6f, 5.2f, 5.8f, 6.4f, 6.6f,
6.4f, 7.0f, 7.6f, 8.2f, 8.4f,
7.0f, 7.6f, 8.2f, 8.8f, 9.0f
};
break;
}
case ResizeMethod::NearestNeighbor:
{
expectedOutputData =
{
1.f, 1.f, 2.f, 2.f, 3.f,
1.f, 1.f, 2.f, 2.f, 3.f,
4.f, 4.f, 5.f, 5.f, 6.f,
4.f, 4.f, 5.f, 5.f, 6.f,
7.f, 7.f, 8.f, 8.f, 9.f
};
break;
}
default:
{
throw InvalidArgumentException("Unrecognized resize method");
}
}
ResizeDescriptor descriptor;
descriptor.m_TargetWidth = outputWidth;
descriptor.m_TargetHeight = outputHeight;
descriptor.m_Method = resizeMethod;
descriptor.m_DataLayout = dataLayout;
// swizzle data if needed
if (dataLayout == armnn::DataLayout::NHWC)
{
constexpr size_t dataTypeSize = sizeof(float);
const armnn::PermutationVector nchwToNhwc = { 0, 3, 1, 2 };
std::vector<float> tmp(inputData.size());
armnnUtils::Permute(inputInfo.GetShape(), nchwToNhwc, inputData.data(), tmp.data(), dataTypeSize);
inputData = tmp;
}
// quantize data
std::vector<T> qInputData = armnnUtils::QuantizedVector<T>(inputData, qScale, qOffset);
std::vector<T> qExpectedOutputData = armnnUtils::QuantizedVector<T>(expectedOutputData, qScale, qOffset);
INetworkPtr network = CreateResizeNetwork(descriptor, inputInfo, outputInfo);
EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network),
{ { 0, qInputData } },
{ { 0, qExpectedOutputData } },
backends);
}
} // anonymous namespace
template<armnn::DataType ArmnnType>
void ResizeBilinearEndToEnd(const std::vector<armnn::BackendId>& backends,
armnn::DataLayout dataLayout)
{
ResizeEndToEnd<ArmnnType>(backends, dataLayout, armnn::ResizeMethod::Bilinear);
}
template<armnn::DataType ArmnnType>
void ResizeNearestNeighborEndToEnd(const std::vector<armnn::BackendId>& backends,
armnn::DataLayout dataLayout)
{
ResizeEndToEnd<ArmnnType>(backends, dataLayout, armnn::ResizeMethod::NearestNeighbor);
}
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